The major thrust of this project is toward development, improvement, and evaluation of quantitative methods and computer techniques for analysis of data from clinical psychopharmacology research. Application of multivariate methods to problems of patient classifiation and patterns of response that are unique to different drug treatments is emphasized. Multidimensional scaling methods will be applied to a variety of types of data descriptive of patient populations for which different drugs are chosen in clinical practice for the purpose of developing models descriptive of differences in the clinical conceptions of optimum use of different psychotherapeutic drugs. Multivariate methods for predicting clinical indications of drugs from pre-clinical animal test data will be explored. Effects of nonlinearity in clinical rating scale data on inferences concerning classification, decisions about drug treatment, and evaluation of treatment outcome will be examined by comparing results from metric and nonmetric methods for multidimensional scaling, profile classification, and tests of significance for treatment effects. Computer programs of general utility for the analysis of clinical research data will be developed and made available to the scientific community as a result of this project.